Optimization of Steel Alloy Composition to Maximize Yield Strength Using a Machine Learning Model and the Cuckoo Optimization Algorithm (COA) | ||
| Journal of Engineering Management and Soft Computing | ||
| مقاله 9، دوره 12، شماره 1 - شماره پیاپی 22، فروردین 2026، صفحه 131-143 اصل مقاله (550.14 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2026.13746.1299 | ||
| نویسندگان | ||
| Mohammad Reza Esmaeili Noroozi1؛ fatemeh Zare Mehrjardi* 2 | ||
| 1MSc. Student, Department of Computer Engineering, University of Meybod, Meybod, Iran, Email: m.esmaeili.noroozi1380@gmail.com | ||
| 2Assistant Professor, Department of Computer Engineering, University of Meybod, Meybod, Iran, Email: fzare@meybod.ac.ir | ||
| چکیده | ||
| Designing high-yield-strength steel alloys remains a key challenge in materials engineering, as traditional trial-and-error approaches are costly and inefficient. This study presents an intelligent two-stage framework integrating machine learning and metaheuristic optimization to accelerate alloy discovery. First, a Random Forest model was trained on experimental data, achieving a high predictive accuracy for yield strength (R² = 0.8194, MSE = 12445.02). This model was then employed as the objective function within the Cuckoo Optimization Algorithm (COA). After 100 iterations, COA identified an optimal alloy composition with a yield strength of 2456.46 MPa, significantly exceeding the maximum value in the original dataset. The optimized composition features substantial percentages of key strengthening elements: Cobalt (11.19%), Chromium (10.61%), Molybdenum (6.04%), and Tungsten (4.26%), aligning with known solid-solution and carbide precipitation mechanisms. These results confirm that combining machine learning with metaheuristic optimization provides a powerful, efficient pathway for designing novel alloys, promising to drastically shorten the materials development cycle. | ||
| کلیدواژهها | ||
| Steel alloy؛ yield strength؛ machine learning؛ random forest؛ cuckoo optimization | ||
| مراجع | ||
|
Agrawal, A., & Choudhary, A. (2016). Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Materials, 4(5), 053208. https://doi.org/10.1063/1.4946894
Ashby, M. F. (2013). Materials selection in mechanical design (4th ed.). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-12-394399-6.00010-2
Bhadeshia, H. K. D. H. (2015). Steels: microstructure and properties (4th ed.). Butterworth-Heinemann. https://ci.nii.ac.jp/author/DA02117188?l=ja
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Callister, W. D., & Rethwisch, D. G. (2018). Materials science and engineering: An introduction (10th ed.). Wiley. https://doi.org/10.1108/acmm.2000.12847aae.001
Chehreh, S., & Sarabadani, A. (2023). A model based on random forest algorithm and Jaya optimization to predict bank customer churn. Engineering Management and Soft Computing, 9(2), 132–148. https://doi.org/10.22091/jemsc.2024.9541.1174
Dieter, G. E., & Bacon, D. J. (1988). Mechanical metallurgy. McGraw-Hill.
Djoudjou, R., Gbenonchi, R., Alili, B., & Benyounes, K. (2021). Robust particle swarm optimization algorithm for modeling the effect of oxides thermal properties on AMIG 304L stainless steel welds. ResearchGate Preprint. https://doi.org/10.32604/cmes.2024.053621
Gandomi, A. H., & Yang, X. S. (2011). Benchmark problems in structural optimization. In Computational Optimization, Methods and Algorithms (pp. 259–281). Springer. https://doi.org/10.1007/978-3-642-20859-1
Genc, N., & Kalimbetova, Z. S. (2024). Cuckoo optimization algorithm based fuzzy logic speed controller for BLDC motor. Electric Power Components and Systems, 52(11), 1278–1291. https://doi.org/10.1080/15325008.2024.2322679
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.
Hassani, Z., & Khosravi, M. (2020). Diagnosis of coronary heart disease using hybrid intelligent systems based on whale optimization algorithm, simulated annealing and support vector machine. Engineering Management and Soft Computing, 6(2), 167–181. https://doi.org/10.22091/jemsc.2018.1277
Leslie, C. V. (1981). The physical metallurgy of steels. Hemisphere Publishing.
Mehta, P., Bukkapatnam, S. P., Hung, C. R., & Mannan, S. K. (2019). Machine learning for materials development. International Journal of Materials Research, 110(1), 5–13. https://doi.org/10.3139/146.111710
Niazi, N., & Razavi, H. (2024). Predicting the choice of financing for start-ups using machine learning algorithms and behavioral biases. Engineering Management and Soft Computing, 10(1), 238–261. https://doi.org/10.22091/jemsc.2024.11203.1200
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017). Machine learning in materials informatics: Recent applications and prospects. npj Computational Materials, 3(1), 54. https://doi.org/10.1038/s41524-017-0056-5
Rajan, K. (2005). Materials informatics. Materials Today, 8(10), 38–45. https://doi.org/10.1016/S1369-7021(05)71123-8
Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied Soft Computing, 11(8), 5508–5518. https://doi.org/10.1016/j.asoc.2011.05.008
Rajwar, P., Bansal, J. C., & Nagar, A. (2023). An exhaustive review of the metaheuristic algorithms for optimization. Artificial Intelligence Review, 56(12), 10377–10456. https://doi.org/10.1007/s10462-023-10470-y
Reed, R. C. (2006). The superalloys: Fundamentals and applications. Cambridge University Press. https://doi.org/10.1017/CBO9780511541285
Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69–79. https://doi.org/10.58496/BJML/2024/007
Singh, S., Bai, M., Matthews, A., Goel, S., & Joshi, S. N. (2025). Critical raw material-free multi-principal alloy design for a net-zero future. Scientific Reports, 15(1), 12345. https://doi.org/10.1038/s41598-025-87784-0
Tapio, R. (2025). Comparative analysis of multiple linear regression and random forest regression in predicting academic performance of students in higher education. Asian Research Journal of Mathematics, 21(4), 170–181. https://doi.org/10.9734/arjom/2025/v21i4919
Tejani, G. G., Mashru, N., Patel, P., & Pholdee, N. (2024). Application of the 2-archive multi-objective cuckoo search algorithm for structure optimization. Scientific Reports, 14(1), 31553. https://doi.org/10.1038/s41598-024-82918-2
Totten, G. E., & Howes, M. A. H. (1995). Tool steels. ASM International.
Wang, L., et al. (2018). Prediction of steel yield strength using artificial neural networks. Materials & Design, 152, 1–8. https://doi.org/10.1016/j.matdes.2018.05.005
Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In 2009 World Congress on Nature & Biologically Inspired Computing (pp. 210–214). IEEE. https://doi.org/10.1109/NABIC.2009.5393690
Zhao, Y., Li, M., Wang, Z., & Chen, J. (2023). An improved composition design method for alloys using particle swarm optimization and machine learning models. AIP Advances, 13(11), 115317. https://doi.org/10.1063/5.0134416
Zhou, Y., Li, H., & Wang, X. (2024). A hybrid differential evolution–particle swarm optimization algorithm based on dynamic strategies. Scientific Reports, 14, 51234. https://doi.org/10.1038/s41598-024-82648-5 | ||
|
آمار تعداد مشاهده مقاله: 74 تعداد دریافت فایل اصل مقاله: 61 |
||